Object recognition device and vehicle travel control system

ABSTRACT

An object recognition device installed on a vehicle includes a first camera detecting a first object, a second camera detecting a second object, a ranging sensor detecting a third object, and a controller. The controller sets first to third determination ranges with respect to detected positions of the first to third objects, respectively, and executes fusion processing by comparing the determination ranges. First and second pixel densities being pixel densities of the first and second objects are determined based on detected distances of the first and second objects, angles of view and numbers of pixels of the first and second cameras, respectively. The controller sets the first determination range larger as the first pixel density is lower, and sets the second determination range larger as the second pixel density is lower.

BACKGROUND Technical Field

The present disclosure relates to an object recognition device and avehicle travel control system installed on a vehicle.

Background Art

Patent Literature 1 discloses an object detection device that detects anobject by using a radar and a monocular camera. The object detectedbased on detection information by the radar is a first object. Theobject detected based on a captured image by the monocular camera is asecond object. The object detection device determines whether or not thefirst object and the second object are close to each other. Morespecifically, the object detection device sets a first error regioncentered at a detected point of the first object and a second errorregion centered at a detected point of the second object. When the firsterror region and the second error region overlap with each other, theobject detection device determines that the first object and the secondobject are close to each other. When the first object and the secondobject are close to each other and a difference in a time-to-collisionwith respect to the two objects is less than a base value, the objectdetection device determines that the first object and the second objectare a same object.

Patent Literature 2 and Patent Literature 3 each also discloses anobject detection device similar to that disclosed in Patent Literature1.

LIST OF RELATED ART

-   Patent Literature 1: Japanese Unexamined Patent Application    Publication No. JP-2016-066182-   Patent Literature 2: Japanese Unexamined Patent Application    Publication No. JP-2017-194432-   Patent Literature 3: Japanese Unexamined Patent Application    Publication No. JP-2016-065760

SUMMARY

As disclosed in the above-mentioned Patent Literature 1, when twoobjects detected by using the radar and the camera satisfy apredetermined condition, the two objects are recognized as the same(identical) object. Such the processing is hereinafter referred to as“fusion processing”. According to the technique disclosed in theabove-mentioned Patent Literature 1, a predetermined determination range(the error region) is used in the fusion processing for determiningwhether or not the two objects are the same object.

Here, let us consider a case where multiple cameras having differentoptical properties are used for detecting an object. In this case,reliability (accuracy) of a detected position of the object is differentfor each camera. Therefore, if the determination range used in thefusion processing is set to have a same size regardless of the cameratype, the determination range may be unreasonably wide or narrow from aviewpoint of true reliability. Setting such the inappropriatedetermination range deteriorates accuracy of the fusion processing andthus causes false recognition of objects.

An object of the present disclosure is to provide an object recognitiontechnique that can execute fusion processing with high accuracy evenwhen multiple cameras having different optical properties are used.

A first disclosure is directed to an object recognition device installedon a vehicle.

The object recognition device includes:

a first camera;

a second camera having an optical property different from that of thefirst camera;

a ranging sensor including at least one of a radar and a LIDAR (LaserImaging Detection and Ranging); and

a control device configured to execute object recognition processing.

The object recognition processing includes:

object detection processing that detects a first object based on aresult of imaging by the first camera, detects a second object based ona result of imaging by the second camera, and detects a third objectbased on a result of measurement by the ranging sensor;

determination range setting processing that sets a first determinationrange with respect to a detected position of the first object, sets asecond determination range with respect to a detected position of thesecond object, and sets a third determination range with respect to adetected position of the third object; and

fusion processing that compares the third determination range with thefirst determination range and the second determination range and, whenthe third object is determined to be a same as at least one of the firstobject and the second object, recognizes the third object and the atleast one as a same object.

A first pixel density being a pixel density of the first object isdetermined based on a detected distance of the first object, an angle ofview and a number of pixels of the first camera.

A second pixel density being a pixel density of the second object isdetermined based on a detected distance of the second object, an angleof view and a number of pixels of the second camera.

In the determination range setting processing, the control device setsthe first determination range larger as the first pixel density islower, and sets the second determination range larger as the secondpixel density is lower.

A second disclosure further has the following feature in addition to thefirst disclosure.

Angle-of-view variations of the first camera and the second cameracaused by oscillation of the vehicle are a first angle-of-view variationand a second angle-of-view variation, respectively.

In the determination range setting processing, the control device setsthe first determination range larger as the first angle-of-viewvariation is larger, and sets the second determination range larger asthe second angle-of-view variation is larger.

A third disclosure is directed to a vehicle travel control system.

The vehicle travel control system includes:

the object recognition device according to the first or seconddisclosure; and

a vehicle travel control device configured to execute vehicle travelcontrol that controls travel of the vehicle based on a result of theobject recognition processing.

Position reliability of the first object is higher as the firstdetermination range is smaller.

Position reliability of the second object is higher as the seconddetermination range is smaller.

The vehicle travel control device sets an upper limit of a controlamount in the vehicle travel control higher as the position reliabilityis higher.

According to the present disclosure, the first determination range andthe second determination range used in the fusion processing arerespectively determined according to pixel densities of the first objectand the second object. As the pixel density is lower, the determinationrange is set to be larger, because reliability of the detected objectposition is lower. That is to say, the first determination range and thesecond determination range are flexibly set according to the reliabilityof the detected object position. Therefore, the first determinationrange and the second determination range are prevented from beingunreasonably wide or narrow. As a result, accuracy of the fusionprocessing increases and thus false recognition of objects issuppressed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram for explaining an object recognitiondevice according to an embodiment of the present disclosure;

FIG. 2 is a block diagram showing a configuration example of the objectrecognition device according to the embodiment of the presentdisclosure;

FIG. 3 is a conceptual diagram for explaining object recognitionprocessing according to the embodiment of the present disclosure;

FIG. 4 is a block diagram showing a functional configuration example ofa control device of the object recognition device according to theembodiment of the present disclosure;

FIG. 5 is a flow chart showing the object recognition processingaccording to the embodiment of the present disclosure;

FIG. 6 is a conceptual diagram for explaining setting of a determinationrange according to a pixel density in the object recognition processingaccording to the embodiment of the present disclosure;

FIG. 7 is a conceptual diagram for explaining setting of thedetermination range in the object recognition processing according tothe embodiment of the present disclosure;

FIG. 8 is a conceptual diagram for explaining setting of thedetermination range according to angle-of-view variation in the objectrecognition processing according to the embodiment of the presentdisclosure;

FIG. 9 is a flow chart showing an example of Step S200 of the objectrecognition processing according to the embodiment of the presentdisclosure;

FIG. 10 is a conceptual diagram for explaining fusion processing in theobject recognition processing according to the embodiment of the presentdisclosure;

FIG. 11 is a block diagram showing a configuration example of a vehicletravel control system according to the embodiment of the presentdisclosure;

FIG. 12 is a conceptual diagram for explaining vehicle travel control bythe vehicle travel control system according to the embodiment of thepresent disclosure; and

FIG. 13 is a conceptual diagram for explaining the vehicle travelcontrol by the vehicle travel control system according to the embodimentof the present disclosure.

EMBODIMENTS

Embodiments of the present disclosure will be described below withreference to the attached drawings.

1. Object Recognition Device

FIG. 1 is a conceptual diagram for explaining an object recognitiondevice 10 according to an embodiment. The object recognition device 10is installed on a vehicle 1 and executes “object recognition processing”that recognizes an object T around the vehicle 1. A result of the objectrecognition processing is used for driving support control, autonomousdriving control, and the like. In the following description, anX-direction represents a longitudinal direction of the vehicle 1 and aY-direction represents a lateral direction of the vehicle 1.

FIG. 2 is a block diagram showing a configuration example of the objectrecognition device 10 according to the present embodiment. The objectrecognition device 10 is provided with a camera device 20, a rangingsensor 30, a vehicle state sensor 40, and a control device (controller)50.

The camera device 20 images a situation around the vehicle 1. In thepresent embodiment, the camera device 20 includes multiple camerashaving different optical properties. For example, the camera device 20includes multiple cameras having different angles of view. In theexample shown in FIG. 2, the camera device 20 includes a first camera 21and a second camera 22. For example, the first camera 21 is a wide anglecamera having a relatively large angle of view, and the second camera 22is a telephoto camera having a relatively small angle of view.

The ranging sensor 30 includes at least one of a radar and a LIDAR(Laser Imaging Detection and Ranging). The ranging sensor 30 outputs atransmission wave and receives a reflected wave from the object T. It ispossible to calculate a position (i.e. a distance and a direction) and avelocity of the object T based on reception state of the reflectedwaves.

The vehicle state sensor 40 detects a sate of the vehicle 1. Forexample, the vehicle state sensor 40 includes a vehicle speed sensor, ayaw rate sensor, and so forth. The vehicle speed sensor detects a speedof the vehicle 1. The yaw rate sensor detects a yaw rate of the vehicle1.

The control device (controller) 50 is a microcomputer including aprocessor and a memory device. The control device 50 is also called anECU (Electronic Control Unit). Processing by the control device 50 isachieved by the processor executing a control program stored in thememory device. The processing by the control device 50 includes theobject recognition processing that recognizes the object T around thevehicle 1. Hereinafter, the object recognition processing by the controldevice 50 will be described.

2. Outline of Object Recognition Processing

The control device 50 detects the object T around the vehicle 1 based ona result of imaging by the camera device 20 and a result of measurementby the ranging sensor 30. The object T detected based on the result ofimaging by the first camera 21 is hereinafter referred to as a “firstobject T21”. The object T detected based on the result of imaging by thesecond camera 22 is hereinafter referred to as a “second object T22”.The object T detected based on the result of measurement by the rangingsensor 30 is hereinafter referred to as a “third object T30”. A detectedposition of each object T is defined by a distance and a direction. Thedetected position (distance, direction) of each object T includes anerror.

Moreover, the control device 50 executes fusion processing. Morespecifically, the control device 50 determines whether or not the thirdobject T30 is the same as at least one of the first object T21 and thesecond object T22. When it is determined that the third object T30 isthe same as at least one of the first object T21 and the second objectT22, the control device 50 recognizes the third object T30 and the atleast one as a same (identical) object.

However, as described above, the detected position of each object Tincludes an error. Therefore, not the detected position itself but a“determination range (error range)” considering the error is used forthe identification determination in the fusion processing. Thedetermination range is a range including the object T.

FIG. 3 conceptually shows the determination range in the XY plane. Afirst determination range R21, which is the determination range withrespect to the detected position of the first object T21, includes thefirst object T21. A second determination range R22, which is thedetermination range with respect to the detected position of the secondobject T22, includes the second object T22. A third determination rangeR30, which is the determination range with respect to the detectedposition of the third object T30, includes the third object T30.Typically, the first determination range R21 and the seconddetermination range R22 are larger than the third determination rangeR30. A shape of each determination range is rectangular in the exampleshown in FIG. 3, but not limited to that.

It should be noted here that the first camera 21 and the second camera22 have different optical properties as described above. Therefore,reliability (accuracy) of the detected position of the first object T21is different from reliability of the detected position of the secondobject T22. If the first determination range R21 and the seconddetermination range R22 are set to have a same size, the determinationrange may be unreasonably wide or narrow from a viewpoint of truereliability. When the first determination range R21 and the seconddetermination range R22 are unreasonably wide or narrow, accuracy of thefusion processing is deteriorated. This causes false recognition ofobjects.

In view of the above, according to the present embodiment, the firstdetermination range R21 and the second determination range R22 are setindependently of each other in consideration of respective opticalproperties of the first camera 21 and the second camera 22. Morespecifically, the first determination range R21 is flexibly setaccording to the reliability of the detected position of the firstobject 121. The first determination range R21 is smaller as thereliability is higher, and the first determination range R21 is largeras the reliability is lower. Similarly, the second determination rangeR22 is flexibly set according to the reliability of the detectedposition of the second object 122. The second determination range R22 issmaller as the reliability is higher, and the second determination rangeR22 is larger as the reliability is lower.

Flexibly setting the first determination range R21 and the seconddetermination range R22 according to the reliability of detected objectposition can prevent the first determination range R21 and the seconddetermination range R22 from being unreasonably wide or narrow. As aresult, accuracy of the fusion processing increases and thus falserecognition of objects is suppressed. Moreover, accuracy of the drivingsupport control and the autonomous driving control utilizing a result ofthe object recognition processing also increases. This contributes toincrease in a driver's confidence in the driving support control and theautonomous driving control.

3. Example of Object Recognition Processing

FIG. 4 is a block diagram showing a functional configuration example ofthe control device 50 of the object recognition device 10 according tothe present embodiment. The control device 50 has an object detectionunit 51, a determination range setting unit 52, and a fusion processingunit 53 as functional blocks. These functional blocks are achieved bythe processor of the control device 50 executing a control programstored in the memory device.

FIG. 5 is a flow chart showing the object recognition processingaccording to the present embodiment. Hereinafter, each Step of theobject recognition processing will be described in detail.

3-1. Step S100 (Object Detection Processing)

The object detection unit 51 detects the first object T21 based on theresult of imaging by the first camera 21. Moreover, the object detectionunit 51 detects the second object T22 based on the result of imaging bythe second camera 22. Then, the object detection unit 51 acquiresinformation of respective detected positions (distances, directions) ofthe first object T21 and the second object T22. Furthermore, the objectdetection unit 51 may calculate respective relative velocities of thefirst object T21 and the second object 722. A method of calculating aposition and a velocity of an object based on imaging information iswell known.

In addition, the object detection unit 51 detects the third object T30based on the result of measurement by the ranging sensor 30. Then, theobject detection unit 51 acquires information of a detected position(distance, direction) of the third object T30. Furthermore, the objectdetection unit 51 may calculate a relative velocity of the third objectT30. A method of calculating a position and a velocity of an objectbased on a result of measurement by the radar or the LIDAR is wellknown.

3-2. Step S200 (Determination Range Setting Processing)

The determination range setting unit 52 sets the determination range(see FIG. 3) used in the subsequent fusion processing (Step S300). Thatis, the determination range setting unit 52 sets the first determinationrange R21 with respect to the detected position of the first object T21.The determination range setting unit 52 sets the second determinationrange R22 with respect to the detected position of the second objectT22. The determination range setting unit 52 sets the thirddetermination range R30 with respect to the detected position of thethird object T30. In the present embodiment, the setting of the thirddetermination range R30 is not limited in particular.

The first determination range R21 is set according to the reliability ofthe detected position of the first object T21. The first determinationrange R21 is smaller as the reliability is higher, and the firstdetermination range R21 is larger as the reliability is lower.

An example of a parameter contributing to the reliability of thedetected position of the first object 121 is a “pixel density” of thefirst object T21 detected. The pixel density of the first object T21 ishereinafter referred to as a “first pixel density”. The first pixeldensity depends on the detected distance of the first object T21, anangle of view and a number of pixels of the first camera 21. The firstpixel density is lower as the detected distance of the first object T21is larger. The first pixel density is lower as the angle of view of thefirst camera 21 is larger. The first pixel density is lower as thenumber of pixels of the first camera 21 is smaller. The determinationrange setting unit 52 calculates the first pixel density based on thedetected distance of the first object T21, the angle of view and thenumber of pixels of the first camera 21. The angle of view and thenumber of pixels of the first camera 21, which are known parameters, arebeforehand registered in the memory device of the control device 50.

FIG. 6 is a conceptual diagram for explaining setting of the firstdetermination range R21 according to the first pixel density. The lowerthe first pixel density is, the lower the reliability of the detectedposition of the first object T21 is. Therefore, the determination rangesetting unit 52 sets the first determination range R21 larger as thefirst pixel density is lower. Conversely, the determination rangesetting unit 52 sets the first determination range R21 smaller as thefirst pixel density is higher. For example, a base pixel density and abase determination range corresponding to the base pixel density are setin advance. The determination range setting unit 52 determines the firstdetermination range R21 by enlarging or reducing the base determinationrange according to a ratio of the first pixel density to the base pixeldensity. It should be noted that the size of the first determinationrange R21 varies in both the X-direction and the Y-direction as shown inFIG. 6.

The same applies to the second determination range R22. The seconddetermination range R22 is set to be larger as the reliability of thedetected position of the second object T22 is lower. The reliability ofthe detected position of the second object T22 depends on a “secondpixel density” being a pixel density of the second object T22 detected.The determination range setting unit 52 calculates the second pixeldensity based on the detected distance of the second object T22, theangle of view and the number of pixels (known parameters) of the secondcamera 22. Then, the determination range setting unit 52 sets the seconddetermination range R22 larger as the second pixel density is lower.Conversely, the determination range setting unit 52 sets the seconddetermination range R22 smaller as the second pixel density is higher.

FIG. 7 shows examples of the first determination range R21 and thesecond determination range R22. In the examples, the first camera 21 isa wide angle camera, the second camera 22 is a telephoto camera, and theangle of view of the first camera 21 is larger than the angle of view ofthe second camera 22. The first determination range R21 and the seconddetermination range R22 each becomes larger with increasing distancefrom the vehicle 1. Under a condition where the distances are the sameand the numbers of pixels are the same, the first determination rangeR21 regarding the first camera 21 having the larger angle of view islarger than the second determination range R22 regarding the secondcamera 22 having the smaller angle of view.

Another example of a parameter contributing to the reliability of thedetected position of the first object T21 is an “angle-of-viewvariation” of the first camera 21 caused by oscillation of the vehicle1. The angle-of-view variation of the first camera 21 caused byoscillation of the vehicle 1 is hereinafter referred to as a “firstangle-of-view variation”. The first angle-of-view variation depends onthe angle of view of the first camera 21 and an oscillating period ofthe vehicle 1. The first angle-of-view variation is larger as the angleof view of the first camera 21 is smaller. The first angle-of-viewvariation is larger as the oscillating period of the vehicle 1 isshorter. The oscillating period of the vehicle 1 is calculated, forexample, from a variation of the yaw rate detected by the vehicle statesensor 40. The determination range setting unit 52 calculates the firstangle-of-view variation based on the angle of view (known parameter) ofthe first camera 21 and the oscillating period of the vehicle 1.

FIG. 8 is a conceptual diagram for explaining setting of the firstdetermination range R21 according to the first angle-of-view variation.The larger the first angle-of-view variation is, the lower thereliability of the detected position of the first object T21 is.Therefore, the determination range setting unit 52 sets the firstdetermination range R21 larger as the first angle-of-view variation islarger. Conversely, the determination range setting unit 52 sets thefirst determination range R21 smaller as the first angle-of-viewvariation is smaller. For example, a base angle-of-view variation and abase determination range corresponding to the base angle-of-viewvariation are set in advance. The determination range setting unit 52determines the first determination range R21 by enlarging or reducingthe base determination range according to a ratio of the firstangle-of-view variation to the base angle-of-view variation.

The same applies to the second determination range R22. The reliabilityof the detected position of the second object T22 depends on a “secondangle-of-view variation” being an angle-of-view variation of the secondcamera 22 caused by the oscillation of the vehicle 1. The determinationrange setting unit 52 calculates the second angle-of-view variationbased on the angle of view (known parameter) of the second camera 22 andthe oscillating period of the vehicle 1. Then, the determination rangesetting unit 52 sets the second determination range R22 larger as thesecond angle-of-view variation is larger. Conversely, the determinationrange setting unit 52 sets the second determination range R22 smaller asthe second angle-of-view variation is smaller.

FIG. 9 is a flow chart showing an example of the determination rangesetting processing (Step S200). In Step S210, the determination rangesetting unit 52 sets the determination range with respect to thedetected position of the detected object according to the pixel densityof the detected object. In Step S220, the determination range settingunit 52 acquires vehicle state information such as the yaw rate detectedby the vehicle state sensor 40. In Step S230, the determination rangesetting unit 52 calculates the angle-of-view variation based on thevehicle state information and sets (modifies) the determination rangeaccording to the angle-of-view variation.

It should be noted that although the determination range is set inconsideration of both the pixel density and the angle-of-view variationin the example shown in FIG. 9, the present embodiment is not limited tothat. Even when one of the pixel density and the angle-of-view variationis taken into consideration, the setting of the determination rangebecomes more appropriate as compared with the conventional techniquewhere none of them is taken into consideration. When both of the pixeldensity and the angle-of-view variation are taken into consideration,the setting of the determination range becomes further more appropriate,which is preferable.

3-3. Step S300 (Fusion Processing)

The fusion processing unit 53 executes the fusion processing. Morespecifically, the fusion processing unit 53 compares the thirddetermination range R30 with the first determination range R21 and thesecond determination range R22 to determine whether or not the thirdobject T30 is the same as at least one of the first object T21 and thesecond object T22. When the third object T30 is determined to be thesame as at least one of the first object T21 and the second object T22,the fusion processing unit 53 recognizes the third object T30 and the atleast one as a same (identical) object.

In the example shown in FIG. 5, not only the determination range butalso the velocity is taken into consideration. More specifically, inStep S310, the fusion processing unit 53 determines whether or not thethird determination range R30 overlaps at least one one of the firstdetermination range R21 and the second determination range R22. FIG. 10shows a case where the third determination range R30 overlaps the firstdetermination range R21 and the second determination range R22. When thethird determination range R30 overlaps at least one of the firstdetermination range R21 and the second determination range R22 (StepS310; Yes), the processing proceeds to Step S320. Otherwise (Step S310;No), the processing proceeds to Step S340.

In Step S320, detected velocities of two objects respectively includedin the overlapping two determination ranges are considered. Morespecifically, the fusion processing unit 53 determines whether or not adifference in the detected velocity between the two objects is equal toor less than a threshold. When the difference in the detected velocityis equal to or less than the threshold (Step S320; Yes), the processingproceeds to Step S330. Otherwise (Step S320; No), the processingproceeds to Step S340.

In Step S330, the fusion processing unit 53 determines that the twoobjects are the same (identical) and recognizes the two objects as thesame object.

In Step S340, the fusion processing unit 53 determines that the twoobjects are separated and recognizes the two objects as separateobjects.

It should be noted that in the fusion processing, consistency of objectsizes and object types (e.g. a vehicle, a pedestrian) may be furthertaken into consideration.

According to the present embodiment, as described above, the firstdetermination range R21 and the second determination range R22 used inthe fusion processing are flexibly set according to the reliability ofthe detected object position. The reliability of the detected objectposition is determined based on at least one of the pixel density andthe angle-of-view variation. Flexible setting according to thereliability can prevent the first determination range R21 and the seconddetermination range R22 from being unreasonably wide or narrow. As aresult, accuracy of the fusion processing increases and thus falserecognition of objects is suppressed. Moreover, accuracy of the drivingsupport control and the autonomous driving control utilizing a result ofthe object recognition processing also increases. This contributes toincrease in a driver's confidence in the driving support control and theautonomous driving control.

4. Vehicle Travel Control System

FIG. 11 is a block diagram showing a configuration example of a vehicletravel control system 100 according to the present embodiment. Thevehicle travel control system 100 is installed on the vehicle 1 andexecutes “vehicle travel control” that controls travel of the vehicle 1.

More specifically, the vehicle travel control system 100 is providedwith a travel device 60 in addition to the above-described objectrecognition device 10. The travel device 60 includes a steering device,a driving device, and a braking device. The steering device turnswheels. The driving device is a power source that generates a drivingforce. The driving device is exemplified by an engine and an electricmotor. The braking device generates a braking force.

The control device 50 executes the vehicle travel control based on aresult of the object recognition by the object recognition device 10.The vehicle travel control includes steering control andacceleration/deceleration control. The control device 50 executes thesteering control by appropriately actuating the steering device.Moreover, the control device 50 executes the acceleration/decelerationcontrol by appropriately actuating the driving device and the brakingdevice. It can be said that the control device 50 and the travel device60 constitute a “vehicle travel control device 70” that executes thevehicle travel control.

In the vehicle travel control, the vehicle travel control device 70considers the reliability of the detected object position. The vehicletravel control device 70 sets an upper limit of a control amount in thevehicle travel control higher as the reliability is higher. That is tosay, the vehicle travel control device 70 permits larger steering andhigher acceleration/deceleration as the reliability is higher. Thus,vehicle behavior is prevented from changing greatly when the reliabilityof the detected object position is low. As a result, the driver'sfeeling of strangeness about the vehicle travel control is suppressed.

FIGS. 12 and 13 are conceptual diagrams for explaining an example of thevehicle travel control considering the reliability of the detectedobject position. A first position reliability S21 is the reliability ofthe detected position of the first object T21 detected by the firstcamera 21. As described above, the first position reliability S21 isassociated with the first determination range R21 and is determinedbased on at least one of the first pixel density and the firstangle-of-view variation. Similarly, a second position reliability S22 isthe reliability of the detected position of the second object 122detected by the second camera 22. As described above, the secondposition reliability S22 is associated with the second determinationrange R22 and is determined based on at least one of the second pixeldensity and the second angle-of-view variation.

In the example shown in FIG. 12, the first position reliability S21 andthe second position reliability S22 are defined with respect to each ofdistance ranges D1 to D4. Among the distance ranges D1 to D4, thedistance range D1 is nearest from the vehicle 1 and the distance rangeD4 is farthest from the vehicle 1. The first camera 21 is a wide anglecamera, and the second camera 22 is a telephoto camera. In the distancerange comparatively near to the vehicle 1, the first positionreliability S21 regarding the first camera 21 is comparatively high. Onthe other hand, in the distance range comparatively far from the vehicle1, the second position reliability S22 regarding the second camera 22 iscomparatively high. An average position reliability SA is an averagevalue of the first position reliability S21 and the second positionreliability S22.

FIG. 13 shows an example of setting of an upper deceleration limit DLaccording to the average position reliability SA. The upper decelerationlimit DL is set to be higher as the average position reliability SA ishigher. Moreover, whether or not the same object is detected by theranging sensor 30 may be taken into consideration. It can be said thatthe position reliability is further higher when the same object isdetected by the ranging sensor 30 (Step S330 in FIG. 5). Therefore, theupper deceleration limit DL is set to be higher as compared with a casewhere the same object is not detected by the ranging sensor 30. When theposition reliability is low, the vehicle travel control device 70 mayissue a warning to the driver without executing the deceleration controland so forth.

What is claimed is:
 1. An object recognition device installed on avehicle and comprising: a first camera; a second camera having anoptical property different from that of the first camera; a rangingsensor including at least one of a radar and a LIDAR (Laser ImagingDetection and Ranging); and a controller configured to execute objectrecognition processing, wherein in the object recognition processing,the controller is configured to: detect a first object based on a resultof imaging by the first camera; detect a second object based on a resultof imaging by the second camera; detect a third object based on a resultof measurement by the ranging sensor; set a first determination rangewith respect to a detected position of the first object; set a seconddetermination range with respect to a detected position of the secondobject; set a third determination range with respect to a detectedposition of the third object; and compare the third determination rangewith the first determination range and the second determination rangeand, when the third object is determined to be a same as at least one ofthe first object and the second object, recognize the third object andthe at least one of the first object and the second object as a sameobject, wherein a first pixel density being a pixel density of the firstobject is determined based on a detected distance of the first object,an angle of view and a number of pixels of the first camera, and asecond pixel density being a pixel density of the second object isdetermined based on a detected distance of the second object, an angleof view and a number of pixels of the second camera, wherein thecontroller is further configured to set the first determination rangelarger as the first pixel density is lower, and to set the seconddetermination range larger as the second pixel density is lower.
 2. Theobject recognition device according to claim 1, wherein angle-of-viewvariations of the first camera and the second camera caused byoscillation of the vehicle are a first angle-of-view variation and asecond angle-of-view variation, respectively, wherein the controller isfurther configure to set the first determination range larger as thefirst angle-of-view variation is larger, and to set the seconddetermination range larger as the second angle-of-view variation islarger.
 3. A vehicle travel control system comprising: the objectrecognition device according to claim 2; and a vehicle travel controllerconfigured to execute vehicle travel control that controls travel of thevehicle based on a result of the object recognition processing, whereinposition reliability of the first object is higher as the firstdetermination range is smaller, and position reliability of the secondobject is higher as the second determination range is smaller, whereinthe vehicle travel controller is further configured to set an upperlimit of a control amount in the vehicle travel control higher as theposition reliability is higher.
 4. A vehicle travel control systemcomprising: the object recognition device according to claim 1; and avehicle travel controller configured to execute vehicle travel controlthat controls travel of the vehicle based on a result of the objectrecognition processing, wherein position reliability of the first objectis higher as the first determination range is smaller, and positionreliability of the second object is higher as the second determinationrange is smaller, wherein the vehicle travel controller is furtherconfigured to set an upper limit of a control amount in the vehicletravel control higher as the position reliability is higher.